Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Priya, C. Sharon Roji; Perumalsamy, Deepalakshmi; Thinakaran, Rajermani Title A name given to the resource GEMS: Gas-Enhanced Marine Search for Optimizing Fusion Mamba-Attention Networks for Fake Review Classification Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Future Internet;Volume;18;Issue;3;Article No.;132; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.3390/fi18030132" target="_blank" rel="noreferrer noopener">https://doi.org/10.3390/fi18030132</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105034257012?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105034257012?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Priya C.S.R., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST University Bangalore, Bangalore, 560074, India; Perumalsamy D., Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, 626128, India; Thinakaran R., Faculty of Data Science and Information Technology, INTI International University, Nilai, 71800, Malaysia Description An account of the resource The rise of fake reviews has become a major problem for trust in e-commerce sites. As for traditional machine learning solutions, they fail to capture the nuanced language that separates real reviews from fake reviews. In this work, we introduce a new hybrid metaheuristic algorithm that optimizes the Fusion Mamba-Attention Network (FMA-Net) for fake review detection, called GEMS (Gas-Enhanced Marine Search). GEMS is a unique combination of the exploration capabilities of the Enhanced Marine Predators Algorithm and the exploitation process of Henry Gas Solubility Optimization, offering a dual-phase optimization design for high-dimensional, asymmetric, metaheuristic-configured GEMS-optimized FMA-Net. Geometric enhancement of GEMS optimization provides GEMS-optimized FMA-Net with an accuracy of 96.8%, F1-score of 95.4%, and AUC-ROC of 97.2%, marking 37% improvement over the current best models for fake review detection on the Yelp, Amazon, and Google Reviews datasets. We lower the average time of hyperparameter optimization using GEMS with FMA-Net to achieve 68% reduction in overall time spent in grid search and 42% lower for complexity in comparison to genetic algorithms. The contributions of this work are the first hybrid metaheuristic for transformers, a mathematically formulated GEMS algorithm, and an extensive empirical study for proving multi-dimensional metric plausibility. 2026 by the authors. Subject The topic of the resource deep learning; fake review detection; henry gas solubility optimization; mamba-attention networks; marine predators algorithm; metaheuristic optimization; sentiment analysis Publisher An entity responsible for making the resource available Multidisciplinary Digital Publishing Institute (MDPI) Relation A related resource ISSN: 19995903; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access; Green Open Access Format The file format, physical medium, or dimensions of the resource online